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Try executing this chunk by clicking the Run button within the chunk or by placing your cursor inside it and pressing Ctrl+Shift+Enter. # Cellchat

source("./tianfengRwrappers.R")

Registered S3 method overwritten by 'data.table':
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载入需要的程辑包：dplyr

载入程辑包：‘dplyr’

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    filter, lag

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    intersect, setdiff, setequal, union

载入需要的程辑包：reticulate
载入需要的程辑包：tidyr

载入程辑包：‘MySeuratWrappers’

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    DimPlot, DoHeatmap, LabelClusters, RidgePlot, VlnPlot


载入程辑包：‘cowplot’

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    get_legend

载入需要的程辑包：viridisLite

载入程辑包：‘reshape2’

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    smiths

NOTE: Either Arial Narrow or Roboto Condensed fonts are required to use these themes.
      Please use hrbrthemes::import_roboto_condensed() to install Roboto Condensed and
      if Arial Narrow is not on your system, please see https://bit.ly/arialnarrow

Registered S3 method overwritten by 'enrichplot':
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clusterProfiler v3.14.3  For help: https://guangchuangyu.github.io/software/clusterProfiler

If you use clusterProfiler in published research, please cite:
Guangchuang Yu, Li-Gen Wang, Yanyan Han, Qing-Yu He. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS: A Journal of Integrative Biology. 2012, 16(5):284-287.
Registering fonts with R

载入程辑包：‘plotly’

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载入需要的程辑包：Biobase
载入需要的程辑包：BiocGenerics
载入需要的程辑包：parallel

载入程辑包：‘BiocGenerics’

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    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ, clusterExport, clusterMap, parApply,
    parCapply, parLapply, parLapplyLB, parRapply, parSapply, parSapplyLB

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    anyDuplicated, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval,
    evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply,
    setdiff, sort, table, tapply, union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

    Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

载入需要的程辑包：e1071

载入程辑包：‘widgetTools’

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载入程辑包：‘DynDoc’

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载入程辑包：‘DT’

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    JS

library(CellChat)

载入需要的程辑包：igraph

载入程辑包：‘igraph’

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CellChat

配体-受体+辅因子

df.net

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cellchat可视化

# pheatmap(mat, display_numbers = FALSE, number_color ="black", cluster_rows = FALSE, 
         # cluster_cols = FALSE, color = colorRampPalette(c("#1E90FF", "white", "#ff2121"))(400), border_color = NA)
table(cor@idents)

       EC1      SMC_3    Mod_SMC Macrophage    T_cell1      SMC_0      SMC_2    T_cell2    T_cell3        EC2 
      4798       1021       1224       9832      12245       3430       1590       4125        366       1294 
    B_cell  Mast_cell      SMC_1 
      2206        430       2294 

mod_SMC – ACKR3

#chord plot
## 免疫细胞 c(4,5,8,9,11) 和SMC c(2,3,6,7,13)的关系
netVisual_chord_gene(cor, sources.use = c(4,5,8,9,11), targets.use = c(2,3,6,7,13), lab.cex = 0.5, legend.pos.y = 30)

##SMC内部
netVisual_chord_gene(cor, sources.use = c(7,13), targets.use = c(2,3,6), lab.cex = 0.5, legend.pos.y = 30)

#指定pathway
pathways.show <- c("TNF") 
# Hierarchy plot
vertex.receiver = c(2,3,6,7,13) # a numeric vector. 
netVisual_aggregate(cor, signaling = pathways.show, layout = "hierarchy", vertex.receiver = vertex.receiver)

netVisual_aggregate(cor, signaling = pathways.show, layout = "circle")

# netVisual_aggregate(cor, signaling = pathways.show, layout = "chord")

# Heatmap
netVisual_heatmap(cor, signaling = pathways.show, color.heatmap = c("#f1f1f1", "#ff2121"))

Do heatmap based on a single object 

#可视化单个配体受体对的影响
netAnalysis_contribution(cor, signaling = pathways.show)

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